How To

The No-Nonsense Guide to Forecasting

How can we improve forecast accuracy? What does best-in-class look like?

And "what are the planning best practices for my industry?"

These are common question we get from consumer goods brands, not to mention topics that immediately catch the attention of anyone in sales operations, planning, or supply chain. Forecasts are never as accurate as you would like, and despite having more sophisticated technologies like AI that we can now throw at the problem, little has changed. A 30% error rate when forecasting SKUs just one month ahead is average in the retail industry, based on IBF surveys.

Getting to single-digit error rates can feel like trying to chase the holy grail. You add more factors into your models, test increasingly sophisticated models based on the latest in data science, and build the business case for a demand management investment to move beyond Excel spreadsheets.

Through it all, though, there’s a tried-and-tested conclusion you should never forget: forecast accuracy is driven by true demand.

No matter what methodology you use, or what types of products you make, a best-in-class forecast is based on unconstrained demand—the amount that you would have sold if the product were always available on the shelf (physical or virtual), when and where a consumer wanted to buy it. It’s not that techniques like machine learning and taking into account causal factors like weather don’t help; they’re just not the biggest difference makers...